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Fitting polytomous Rasch models in SAS.

Karl Bang Christensen1

  • 1National Institute of Occupational Health, Denmark, Lerso Parkalle 105, Copenhagen 2100 O, Denmark. kbc@ami.dk

Journal of Applied Measurement
|October 28, 2006
PubMed
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This study details item parameter estimation for polytomous Rasch models in SAS, comparing marginal maximum likelihood, conditional maximum likelihood, and pairwise conditional methods. Accuracy was assessed via simulation, offering insights into psychometric modeling techniques.

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Educational Measurement

Background:

  • Item parameter estimation is crucial for polytomous Rasch models.
  • Marginal and conditional approaches are common estimation methods.
  • Software implementation facilitates model application.

Purpose of the Study:

  • To describe SAS V8.2 procedures for estimating polytomous Rasch model item parameters.
  • To compare marginal maximum likelihood, conditional maximum likelihood, and pairwise conditional estimation.
  • To discuss the application of these procedures to Rasch model extensions.

Main Methods:

  • Implementation of marginal maximum likelihood estimation in SAS.
  • Implementation of conditional maximum likelihood estimation in SAS.

Related Experiment Videos

  • Implementation of pairwise conditional estimation in SAS.
  • Evaluation of accuracy using a simulation study.

    • Main Results:

    • The paper provides SAS code and procedures for three estimation methods.
    • A simulation study was conducted to evaluate the accuracy of the implemented methods.
    • The study discusses extensions of the Rasch model and their parameter estimation.

    Conclusions:

    • SAS procedures enable efficient estimation of item parameters for polytomous Rasch models.
    • The simulation study offers empirical evidence on the accuracy of different estimation approaches.
    • The findings support the use of these methods for both standard and extended Rasch models.